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arXiv Machine Learning

Inductive Correlation Clustering with Graph Neural Networks

The paper introduces Inductive Correlation Clustering, a new framework that uses Graph Neural Networks to solve the Correlation Clustering problem on unseen graph instances. By learning common structural patterns and node features, the method generalizes to new graphs with minimal computational overhead, achieving inference times up to five orders of magnitude faster while maintaining an approximation ratio within about 10% of the best baseline. It also demonstrates competitive performance on standard transductive benchmarks and serves as an efficient learnable pooling layer for graph classification tasks.

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arXiv AI
Oct 7

Towards One-for-All Foundation Model for Attributed Graph Clustering

The paper introduces OFAG, a foundation model designed for attributed graph clustering that can be trained once and applied to diverse graphs without graph‑specific tuning. It learns a reusable clustering strategy from synthetic graphs and uses a dimension‑agnostic encoder to handle varying feature spaces, producing clustering‑friendly node representations in a single forward pass. Across ten datasets, a frozen OFAG model outperforms baselines in both speed and clustering quality, and the authors provide code and pretrained checkpoints for easy adoption.

By Yunhui Liu, Xudong Jin, Kang Zhang, Danshuo An, Yu Xing, Te Song, Jia Liu, Tieke He
fine-tuning
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Hugging Face Trending Papers
Aug 3

CoRe-GNN: Multilevel Message passing on Coarsened graphs

Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified perspective that exposes their respective limitations.

benchmarks
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arXiv AI
Oct 9

Compactness and Consistency: A Conjoint Framework for Deep Graph Clustering

The paper introduces CoCo, a conjoint framework for deep graph clustering that emphasizes compactness and consistency in node representations. CoCo uses graph convolutional filters to learn robust embeddings from both local and global views, then encodes them into low‑rank compact embeddings to reduce redundancy and noise. A consistency learning strategy further enriches node semantics, enabling knowledge transfer between perspectives and yielding superior performance over state‑of‑the‑art methods on multiple datasets.

By Wei Ju, Siyu Yi, Kangjie Zheng, Yifan Wang, Ziyue Qiao, Li Shen, Yongdao Zhou, Xiaochun Cao, Jiancheng Lv
ragbenchmarks
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arXiv AI
Aug 10

Cluster Attention for Graph Machine Learning

arXiv:2604. 07492v2 Announce Type: replace-cross Abstract: Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field is limited by the number of message passing layers.

By Oleg Platonov, Liudmila Prokhorenkova
llmsbenchmarks
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arXiv Machine Learning
Sep 11

SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking

SynCo is a synthetic graph generator that lets users control node degree distributions and sub‑community structures, addressing limitations of existing generators that rely on power‑law distributions and lack flexibility. It is evaluated on graph mimicking, hyperparameter tuning, and node clustering, outperforming state‑of‑the‑art methods while preserving original data distributions. SynCo can generate large graphs with up to 2.1 million nodes.

By Guilherme Henrique Messias, Mariana Caravanti de Souza, Sylvia Iasulaitis, Alan Dem\'etrius Baria Valejo
benchmarks
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